
DeepWalk [1]}: This method extract d-dimensional features based on uniform random walks

Node2Vec [2]}: This method learns d-dimensional features using biased random walk strategy which helps in flexible exploration of neighborhood. We set the parameters for exploring the neighborhood nodes: p and q as 1

OhmNet [3]}: This approach uses Node2Vec method for feature learning, but for a multilayer network setup. This approach defines a hierarchy for representing nodes from multiple layers. We use same parameters as Node2Vec and we create a 2-level hierarchy for all multiplex networks.

MNE [4]}: This latest approach is similar to Node2Vec. This approach considers a base network of a multiplex network to learn the embedding of all nodes and individual representation for each layer. Since this approach uses Node2Vec as a base method, we use same parameters as Node2Vec algorithm.

